Odesso, a leading enterprise no-code SaaS provider that helps digital businesses accelerate three times faster than traditional low/no-code and API management products, today announced its Appian Performance Tuner (APT). The APT enables organizations using Appian to accelerate their workflow automation investments by speeding up applications as much as 50 percent.
APT is the best option when critical applications are slow and a reactive approach is needed. APT is also customized to critically monitor apps and the areas of highest vulnerability in performance adding a layer of extra protection to the general health check report and performance analytics. The APT takes a proactive instead of reactive approach to the tools that Appian provides because, if only using Appian, administrators must manually check to monitor problems.
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The Appian Performance Tuner (APT) is Odesso’s latest product to speed up workflow automation applications, often achieving over 90% improvement compared to previous speeds. It scales workflow automation investment with ease over internet connections. This allows an organization’s customers and employees to continue accelerating business while the orchestrated processes, organizational data, and securitization mechanisms are responsive and resilient to unexpected workloads.
The APT created by Odesso is engineered to identify and remedy latency issues (potentially residing in more than one layer of the technology stack) quickly and with minimal impact on business continuity.
“We are proud to be releasing the Appian Performance Tuner,” said Zee Rizvi, CEO of Odesso. “Performance problems impact everything from customer experience to security and compliance. APT offers a fast and cost effective way to stop the bleeding ASAP.”
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How the Appian Performance Tuner Works:
APT is an adaptive technology that can be deployed in Appian’s AWS Cloud installation or in an On-Premise setting. APT is not an analytics-only solution, it remedies performance issues instead of simply identifying them. APT works in the following steps:
- Application Analysis
Beyond the Health Check, first the APT analyzes the Appian processes, SAIL, data structures, and transactional throughput metrics in real-time. - Convert
Odesso’s proprietary method to convert interchangeable objects between SAIL, SQL, and Process Models (BPMN) to quickly redesign non-performant objects. - Instrument
Odesso provides custom Instrumentation of the most critical business processes at the application layer. - Data
APT provides data-centric analysis, monitoring, and notifications within the workflow automation application and engine layers. - Transactional Throttling
The Transactional Throttling features batch, spawn, queue, lock, optimize and add exception handling to high data loads. - Stop Data Loss
Queue important transactions when systems or connections are unstable so they’re not lost. - Apache Kafka Streaming
Initialize Kafka to enhance the responsiveness of data-intensive interfaces and provide real-time application streaming. - Server Resource Optimization
Provides server resource optimization to reallocate network/compute/storage resources to critical business functions.
A well-designed Appian application is data-centric and aimed to be the fulcrum upon which the overlying operational business workflows rest. This data is often millions of records, supplemented by service-backed data from third-party data sources that are brought in via FTP or Web Service connections. The power of Appian applications depends heavily on the efficiency of data throughput and the design of the data with its related keys and metadata. While most expert Appian developers know how to do this from the onset of creating a new app, many mid to junior developers do not. This leads to layers of nested design patterns in data objects, reducing the ability to index, search and process data efficiently. The APT identifies these design issues with its proprietary methods, improving data processing efficiency.
Often newly deployed Appian applications are rarely tested with realistic production data loads and provisioning mechanisms. Therefore, a new application performs well when it’s deployed, but as concurrent users, data volumes, external services and transactional processing loads peak in a production setting, the responsiveness of the application very quickly dwindles.
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